Finding driver pathways in cancer: models and algorithms
<p>Abstract</p> <p>Background</p> <p>Cancer sequencing projects are now measuring somatic mutations in large numbers of cancer genomes. A key challenge in interpreting these data is to distinguish <it>driver mutations</it>, mutations important for cancer dev...
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| Format: | Article |
| Language: | English |
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BMC
2012-09-01
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| Series: | Algorithms for Molecular Biology |
| Subjects: | |
| Online Access: | http://www.almob.org/content/7/1/23 |
| _version_ | 1818756617774039040 |
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| author | Vandin Fabio Upfal Eli Raphael Benjamin J |
| author_facet | Vandin Fabio Upfal Eli Raphael Benjamin J |
| author_sort | Vandin Fabio |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Cancer sequencing projects are now measuring somatic mutations in large numbers of cancer genomes. A key challenge in interpreting these data is to distinguish <it>driver mutations</it>, mutations important for cancer development, from <it>passenger</it> mutations that have accumulated in somatic cells but without functional consequences. A common approach to identify genes harboring driver mutations is a <it>single gene test</it> that identifies individual genes that are recurrently mutated in a significant number of cancer genomes. However, the power of this test is reduced by: (1) the necessity of estimating the <it>background mutation rate</it> (BMR) for each gene; (2) the mutational heterogeneity in most cancers meaning that groups of genes (e.g. pathways), rather than single genes, are the primary target of mutations.</p> <p>Results</p> <p>We investigate the problem of discovering <it>driver pathways</it>, groups of genes containing driver mutations, directly from cancer mutation data and without prior knowledge of pathways or other interactions between genes. We introduce two generative models of somatic mutations in cancer and study the algorithmic complexity of discovering driver pathways in both models. We show that a single gene test for driver genes is highly sensitive to the estimate of the BMR. In contrast, we show that an algorithmic approach that maximizes a straightforward measure of the mutational properties of a driver pathway successfully discovers these groups of genes without an estimate of the BMR. Moreover, this approach is also successful in the case when the observed frequencies of passenger and driver mutations are indistinguishable, a situation where single gene tests fail.</p> <p>Conclusions</p> <p>Accurate estimation of the BMR is a challenging task. Thus, methods that do not require an estimate of the BMR, such as the ones we provide here, can give increased power for the discovery of driver genes.</p> |
| first_indexed | 2024-12-18T05:57:54Z |
| format | Article |
| id | doaj.art-b49898a60c7d41c3987d11091ca9fd3e |
| institution | Directory Open Access Journal |
| issn | 1748-7188 |
| language | English |
| last_indexed | 2024-12-18T05:57:54Z |
| publishDate | 2012-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Algorithms for Molecular Biology |
| spelling | doaj.art-b49898a60c7d41c3987d11091ca9fd3e2022-12-21T21:18:45ZengBMCAlgorithms for Molecular Biology1748-71882012-09-01712310.1186/1748-7188-7-23Finding driver pathways in cancer: models and algorithmsVandin FabioUpfal EliRaphael Benjamin J<p>Abstract</p> <p>Background</p> <p>Cancer sequencing projects are now measuring somatic mutations in large numbers of cancer genomes. A key challenge in interpreting these data is to distinguish <it>driver mutations</it>, mutations important for cancer development, from <it>passenger</it> mutations that have accumulated in somatic cells but without functional consequences. A common approach to identify genes harboring driver mutations is a <it>single gene test</it> that identifies individual genes that are recurrently mutated in a significant number of cancer genomes. However, the power of this test is reduced by: (1) the necessity of estimating the <it>background mutation rate</it> (BMR) for each gene; (2) the mutational heterogeneity in most cancers meaning that groups of genes (e.g. pathways), rather than single genes, are the primary target of mutations.</p> <p>Results</p> <p>We investigate the problem of discovering <it>driver pathways</it>, groups of genes containing driver mutations, directly from cancer mutation data and without prior knowledge of pathways or other interactions between genes. We introduce two generative models of somatic mutations in cancer and study the algorithmic complexity of discovering driver pathways in both models. We show that a single gene test for driver genes is highly sensitive to the estimate of the BMR. In contrast, we show that an algorithmic approach that maximizes a straightforward measure of the mutational properties of a driver pathway successfully discovers these groups of genes without an estimate of the BMR. Moreover, this approach is also successful in the case when the observed frequencies of passenger and driver mutations are indistinguishable, a situation where single gene tests fail.</p> <p>Conclusions</p> <p>Accurate estimation of the BMR is a challenging task. Thus, methods that do not require an estimate of the BMR, such as the ones we provide here, can give increased power for the discovery of driver genes.</p>http://www.almob.org/content/7/1/23CancerSomatic MutationsDriver mutationsPathwaysBackground mutation rateGenerative models |
| spellingShingle | Vandin Fabio Upfal Eli Raphael Benjamin J Finding driver pathways in cancer: models and algorithms Algorithms for Molecular Biology Cancer Somatic Mutations Driver mutations Pathways Background mutation rate Generative models |
| title | Finding driver pathways in cancer: models and algorithms |
| title_full | Finding driver pathways in cancer: models and algorithms |
| title_fullStr | Finding driver pathways in cancer: models and algorithms |
| title_full_unstemmed | Finding driver pathways in cancer: models and algorithms |
| title_short | Finding driver pathways in cancer: models and algorithms |
| title_sort | finding driver pathways in cancer models and algorithms |
| topic | Cancer Somatic Mutations Driver mutations Pathways Background mutation rate Generative models |
| url | http://www.almob.org/content/7/1/23 |
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